Chapter 7,Support Vector Machines, introduces this family of algorithms, focusing on both linear and nonlinear classification problems. Chapter 8, Decision Trees and Ensemble Learning, explains the concept of a hierarchical decision process and describes the concepts of decision tree classification, Bootstrap and bagged trees, and voting classifiers.
Chapter 10, Hierarchical Clustering, continues the explanation started in the previous chapter and introduces the concept of agglomerative clustering. Chapter 11,Introduction to Recommendation Systems, explains the most diffused algorithms employed in recommender systems: content- and user- based strategies, collaborative filtering, and alternating least square.
Chapter 12,Introduction to Natural Language Processing, explains the concept of bag-of-words and introduces the most important techniques required to efficiently process natural language datasets. Chapter 13, Topic Modeling and Sentiment Analysis in NLP, introduces the concept of topic modeling and describes the most important algorithms, such as latent semantic analysis and latent Dirichlet allocation.
In the second part, the chapter covers the problem of sentiment analysis, explaining the most diffused approaches to address it. Chapter 14,A Brief Introduction to Deep Learning and TensorFlow, introduces the world of deep learning, explaining the concept of neural networks and computational graphs.
The second part is dedicated to a brief exposition of the main concepts regarding the TensorFlow and Keras frameworks, with some practical examples. Creating a Machine Learning Architecture, explains how to define Chapter 15, a complete machine learning pipeline, focusing on the peculiarities and drawbacks of each step. What you need for this book There are no particular mathematical prerequisites; however, to fully understand all the algorithms, it's important to have a basic knowledge of linear algebra, probability theory, and calculus.
When a particular framework is employed for a specific task, detailed instructions and references will be provided. Who this book is for This book is for IT professionals who want to enter the field of data science and are very new to machine learning. Familiarity with the Python language will be invaluable here. Moreover, basic mathematical knowledge linear algebra, calculus, and probability theory is required to fully comprehend the content of most of the chapters.
Conventions In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We have created a configuration through the SparkConf class.
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A Gentle Introduction to Machine Learning In the last few years, machine learning has become one of the most important and prolific IT and artificial intelligence branches.
It's not surprising that its applications are becoming more widespread day by day in every business sector, always with new and more powerful tools and results. Open source, production-ready frameworks, together with hundreds of papers published every month, are contributing to one of the most pervasive democratization processes in IT history.
But why is machine learning so important and valuable? Introduction - classic and adaptive machines Since time immemorial, human beings have built tools and machines to simplify their work and reduce the overall effort needed to complete many different tasks. Even without knowing any physical law, they invented levers formally described for the first time by Archimedes , instruments, and more complex machines to carry out longer and more sophisticated procedures.
Hammering a nail became easier and more painless thanks to a simple trick, so did moving heavy stones or wood using a cart. Even if the latter is still a simple machine, its complexity allows a person to carry out a composite task without thinking about each step. Some fundamental mechanical laws play a primary role in allowing a horizontal force to contrast gravity efficiently, but neither human beings nor horses or oxen knew anything about them.
The primitive people simply observed how a genial trick the wheel could improve their lives. The lesson we've learned is that a machine is never efficient or trendy without a concrete possibility to use it with pragmatism. A machine is immediately considered useful and destined to be continuously improved if its users can easily understand what tasks can be completed with less effort or completely automatically.
In the latter case, some intelligence seems to appear next to cogs, wheels, or axles. Wind or watermills are some examples of elementary tools able to carry out complete tasks with minimal compared to a direct activity human control.
Both the water in a river and the wind show a behavior that we can simply call flowing. They have a lot of energy to give us free of any charge, but a machine should have some awareness to facilitate this process.
A wheel can turn around a fixed axle millions of times, but the wind must find a suitable surface to push on. The answer seems obvious, but you should try to think about people without any knowledge or experience; even if implicitly, they started a brand new approach to technology.
Without further intermediate but not less important steps, we can jump into our epoch and change the scope of our discussion. Programmable computers are widespread, flexible, and more and more powerful instruments; moreover, the diffusion of the internet allowed us to share software applications and related information with minimal effort. The word- processing software that I'm using, my email client, a web browser, and many other common tools running on the same machine are all examples of such flexibility.
It's undeniable that the IT revolution dramatically changed our lives and sometimes improved our daily jobs, but without machine learning and all its applications , there are still many tasks that seem far out of computer domain.
In many cases, they transformed our electronic tools into actual cognitive extensions that are changing the way we interact with many daily situations.
They achieved this goal by filling the gap between human perception, language, reasoning, and model and artificial instruments. Such a system isn't based on static or permanent structures model parameters and architectures but rather on a continuous ability to adapt its behavior to external signals datasets or real-time inputs and, like a human being, to predict the future using uncertain and fragmentary pieces of information.
Only learning matters What does learning exactly mean? Simply, we can say that learning is the ability to change according to external stimuli and remembering most of all previous experiences. So machine learning is an engineering approach that gives maximum importance to every technique that increases or improves the propensity for changing adaptively.
A mechanical watch, for example, is an extraordinary artifact, but its structure obeys stationary laws and becomes useless if something external is changed.
Machines, even if they don't evolve autonomously, seem to obey the same law. Therefore, the main goal of machine learning is to study, engineer, and improve mathematical models which can be trained once or continuously with context-related data provided by a generic environment , to infer the future and to make decisions without complete knowledge of all influencing elements external factors.
In other words, an agent which is a software entity that receives information from an environment, picks the best action to reach a specific goal, and observes the results of it adopts a statistical learning approach, trying to determine the right probability distributions and use them to compute the action value or decision that is most likely to be successful with the least error.
I do prefer using the term inference instead of prediction only to avoid the weird but not so uncommon idea that machine learning is a sort of modern magic. Moreover, it's possible to introduce a fundamental statement: an algorithm can extrapolate general laws and learn their structure with relatively high precision only if they affect the actual data. So the term prediction can be freely used, but with the same meaning adopted in physics or system theory. In the next sections, there's a brief description of some common approaches to machine learning.
Mathematical models, algorithms, and practical examples will be discussed in later chapters. Supervised learning A supervised scenario is characterized by the concept of a teacher or supervisor, whose main task is to provide the agent with a precise measure of its error directly comparable with output values. With actual algorithms, this function is provided by a training set made up of couples input and expected output. Starting from this information, the agent can correct its parameters so as to reduce the magnitude of a global loss function.
After each iteration, if the algorithm is flexible enough and data elements are coherent, the overall accuracy increases and the difference between the predicted and expected value becomes close to zero. Of course, in a supervised scenario, the goal is training a system that must also work with samples never seen before. So, it's necessary to allow the model to develop a generalization ability and avoid a common problem called overfitting, which causes an overlearning due to an excessive capacity we're going to discuss this in more detail in the next chapters, however we can say that one of the main effects of such a problem is the ability to predict correctly only the samples used for training, while the error for the remaining ones is always very high.
The former is unacceptable because it cannot generalize and capture the fastest dynamics in terms of frequency , while the latter seems a very good compromise between the original trend and a residual ability to generalize correctly in a predictive analysis.
Formally, the previous example is called regression because it's based on continuous output values. Instead, if there is only a discrete number of possible outcomes called categories , the process becomes a classification. Sometimes, instead of predicting the actual category, it's better to determine its probability distribution. For example, an algorithm can be trained to recognize a handwritten alphabetical letter, so its output is categorical in English, there'll be 26 allowed symbols.
On the other hand, even for human beings, such a process can lead to more than one probable outcome when the visual representation of a letter isn't clear enough to belong to a single category. That means that the actual output is better described by a discrete probability distribution for example, with 26 continuous values normalized so that they always sum up to 1.
The majority of algorithms try to find the best separating hyperplane in this case, it's a linear problem by imposing different conditions. However, the goal is always the same: reducing the number of misclassifications and increasing the noise-robustness.
For example, look at the triangular point that is closer to the plane its coordinates are about [5. If the magnitude of the second feature were affected by noise and so the value were quite smaller than 3. We're going to discuss some powerful techniques to solve these problems in later chapters. For example, looking at the previous figure, a human being can immediately identify two sets without considering the colors or the shapes.
In fact, the circular dots as well as the triangular ones determine a coherent set; it is separate from the other one much more than how its points are internally separated. Using a metaphor, an ideal scenario is a sea with a few islands that can be separated from each other considering only their mutual position and internal cohesion. In the next figure, each ellipse represents a cluster and all the points inside its area can be labeled in the same way.
There are also boundary points such as the triangles overlapping the circle area that need a specific criterion normally a trade-off distance measure to determine the corresponding cluster. Just as for classification with ambiguities P and malformed R , a good clustering approach should consider the presence of outliers and treat them so as to increase both the internal coherence visually, this means picking a subdivision that maximizes the local density and the separation among clusters.
For example, it's possible to give priority to the distance between a single point and a centroid, or the average distance among points belonging to the same cluster and different ones. In this figure, all boundary triangles are close to each other, so the nearest neighbor is another triangle. However, in real- life problems, there are often boundary areas where there's a partial overlap, meaning that some points have a high degree of uncertainty due to their feature values.
Another interpretation can be expressed using probability distributions. If you look at the ellipses, they represent the area of multivariate Gaussians bound between a minimum and maximum variance. Considering the whole domain, a point for example, a blue star could potentially belong to all clusters, but the probability given by the first one lower-left corner is the highest, and so this determines the membership.
Once the variance and mean in other words, the shape of all Gaussians become stable, each boundary point is automatically captured by a single Gaussian distribution except in the case of equal probabilities. Technically, we say that such an approach maximizes the likelihood of a Gaussian mixture given a certain dataset. This is a very important statistical learning concept that spans many different applications, so it will be examined in more depth in the next chapter.
Moreover, we're going to discuss some common clustering methodologies, considering both strong and weak points and comparing their performances for various test distributions. Other important techniques involve the usage of both labeled and unlabeled data.
However, in this case, the information is more qualitative and doesn't help the agent in determining a precise measure of its error. In reinforcement learning, this feedback is usually called reward sometimes, a negative one is defined as a penalty and it's useful to understand whether a certain action performed in a state is positive or not.
The sequence of most useful actions is a policy that the agent has to learn, so to be able to make always the best decision in terms of the highest immediate and cumulative reward. In other words, an action can also be imperfect, but in terms of a global policy it has to offer the highest total reward.
The ability to see over a distant horizon is a distinction mark for advanced agents, while short- sighted ones are often unable to correctly evaluate the consequences of their immediate actions and so their strategies are always sub-optimal. Reinforcement learning is particularly efficient when the environment is not completely deterministic, when it's often very dynamic, and when it's impossible to have a precise error measure.
During the last few years, many classical algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state usually a screenshot or a memory dump. In the following figure, there's a schematic representation of a deep neural network trained to play a famous Atari game.
As input, there are one or more subsequent screenshots this can often be enough to capture the temporal dynamics as well. They are processed using different layers discussed briefly later to produce an output that represents the policy for a specific state transition. We're going to discuss some examples of reinforcement learning in the chapter dedicated to introducing deep learning and TensorFlow. Beyond machine learning - deep learning and bio-inspired adaptive systems During the last few years, thanks to more powerful and cheaper computers, many researchers started adopting complex deep neural architectures to achieve goals there were unimaginable only two decades ago.
Since , when Rosenblatt invented the first perceptron, interest in neural networks has grown more and more. However, many limitations concerning memory and CPU speed prevented massive research and hid lots of potential applications of these kinds of algorithms. In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called deep learning , to solve new challenging problems.
The availability of cheap and fast computers allowed them to get results in acceptable timeframes and to use very large datasets made up of images, texts, and animations. This effort led to impressive results, in particular for classification based on photo elements and real-time intelligent interaction using reinforcement learning. The idea behind these techniques is to create algorithms that work like a brain and many important advancements in this field have been achieved thanks to the contribution of neurosciences and cognitive psychology.
In particular, there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called model-free; these aren't based on any mathematical-physical formulation of a particular problem but rather on generic learning techniques and repeating experiences.
Of course, testing different architectures and optimization algorithms is quite simpler and it can be done with parallel processing than defining a complex model which is also more difficult to adapt to different contexts.
This suggests that in many cases, it's better to have a less precise decision made with uncertainty than a precise one determined by the output of a very complex model often not so fast. For animals, this is often a matter of life and death, and if they succeed, it is thanks to an implicit renounce of some precision.
Image classification Real-time visual tracking Autonomous car driving Logistic optimization Bioinformatics Speech recognition. Many of these problems can also be solved using classic approaches, sometimes much more complex, but deep learning outperformed them all. Moreover, it allowed extending their application to contexts initially considered extremely complex, such as autonomous cars or real-time visual object identification. This book covers in detail only some classical algorithms; however, there are many resources that can be read both as an introduction and for a more advanced insight.
Machine learning and big data Another area that can be exploited using machine learning is big data. After the first release of Apache Hadoop, which implemented an efficient MapReduce algorithm, the amount of information managed in different business contexts grew exponentially.
At the same time, the opportunity to use it for machine learning purposes arose and several applications such as mass collaborative filtering became reality.
Imagine an online store with a million users and only one thousand products. Consider a matrix where each user is associated with every product by an implicit or explicit ranking. This matrix will contain 1,, x 1, cells, and even if the number of products is very limited, any operation performed on it will be slow and memory-consuming.
Instead, using a cluster, together with parallel algorithms, such a problem disappears and operations with higher dimensionality can be carried out in a very short time. Think about training an image classifier with a million samples. A single instance needs to iterate several times, processing small batches of pictures.
Even if this problem can be performed using a streaming approach with a limited amount of memory , it's not surprising to wait even for a few days before the model begins to perform well. Adopting a big data approach instead, it's possible to asynchronously train several local models, periodically share the updates, and re-synchronize them all with a master model. This technique has also been exploited to solve some reinforcement learning problems, where many agents often managed by different threads played the same game, providing their periodical contribute to a global intelligence.
Not every machine learning problem is suitable for big data, and not all big datasets are really useful when training models. However, their conjunction in particular situations can drive to extraordinary results by removing many limitations that often affect smaller scenarios. In the chapter dedicated to recommendation systems, we're going to discuss how to implement collaborative filtering using Apache Spark. The same framework will be also adopted for an example of Naive Bayes classification.
Further reading An excellent introduction to artificial intelligence can be found in the first few chapters of Russel S. In the second volume, there's also a very extensive discussion on statistical learning in many different contexts. A complete book on deep learning is Goodfellow I. If you would like to learn more about how the neocortex works, a simple but stunning introduction is present in Kurzweil R. A comprehensive introduction to the Python programming language can be found in Lutz M.
Summary In this chapter, we introduced the concept of adaptive systems; they can learn from their experiences and modify their behavior in order to maximize the possibility of reaching a specific goal. Machine learning is the name given to a set of techniques that allow implementing adaptive algorithms to make predictions and to auto-organize input data according to their common features.
The three main learning strategies are supervised, unsupervised, and reinforcement. The first one assumes the presence of a teacher that provides a precise feedback on errors.
The algorithm can hence compare its output with the right one and correct its parameters accordingly. In an unsupervised scenario, there are no external teachers, so everything is learned directly from the data. An algorithm will try to find out all features common to a group of elements to be able to associate new samples with the right cluster. Examples of the former type are provided by all the automatic classifications of objects into a specific category according to some known features, while common applications of unsupervised learning are the automatic groupings of items with a subsequent labeling or processing.
The third kind of learning is similar to supervised, but it receives only an environmental feedback about the quality of its actions. It doesn't know exactly what is wrong or the magnitude of its error but receives generic information that helps it in deciding whether to continue to adopt a policy or to pick another one. In the next chapter, we're going to discuss some fundamental elements of machine learning, with particular focus on the mathematical notation and the main definitions that we'll need in all the other chapters.
We'll also discuss important statistical learning concepts and some theory about learnability and its limits. Important Elements in Machine Learning In this chapter, we're going to discuss some important elements and approaches which span through all machine learning topics and also create a philosophical foundation for many common techniques. First of all, it's useful to understand the mathematical foundation of data formats and prediction functions. In most algorithms, these concepts are treated in different ways, but the goal is always the same.
Data formats In a supervised learning problem, there will always be a dataset, defined as a finite set of real vectors with m features each:. Considering that our approach is always probabilistic, we need to consider each X as drawn from a statistical multivariate distribution D. For our purposes, it's also useful to add a very important condition upon the whole dataset X: we expect all samples to be independent and identically distributed i.
This means all variables belong to the same distribution D, and considering an arbitrary subset of m values, it happens that:. The corresponding output values can be both numerical-continuous or categorical.
In the first case, the process is called regression, while in the second, it is called classification. Examples of numerical outputs are:. We define generic regressor, a vector-valued function which associates an input value to a continuous output and generic classifier, a vector-values function whose predicted output is categorical discrete. A very common non-parametric family is called instance-based learning and makes real-time predictions without pre-computing parameter values based on hypothesis determined only by the training samples instance set.
A simple and widespread approach adopts the concept of neighborhoods with a fixed radius. In a classification problem, a new sample is automatically surrounded by classified training elements and the output class is determined considering the preponderant one in the neighborhood.
In this book, we're going to talk about another very important algorithm family belonging to this class: kernel- based support vector machines.
More examples can be found in Russel S. The internal dynamics and the interpretation of all elements are peculiar to each single algorithm, and for this reason, we prefer not to talk now about thresholds or probabilities and try to work with an abstract definition.
For our purposes, we can expect zero-mean and low-variance Gaussian noise added to a perfect prediction. A training task must increase the signal-noise ratio by optimizing the parameters.
On the other hand, high noise variance means that X is dirty and its measures are not reliable. Until now we've assumed that both regression and classification operate on m-length vectors but produce a single value or single label in other words, an input vector is always associated with only one output element.
However, there are many strategies to handle multi-label classification and multi-output regression. In unsupervised learning, we normally only have an input set X with m-length vectors, and we define clustering function with n target clusters with the following expression:.
In both cases, the choice is transparent and the output returned to the user will always be the final value or class. However, it's important to understand the different dynamics in order to optimize the model and to always pick the best alternative. One-vs-all This is probably the most common strategy and is widely adopted by scikit- learn for most of its algorithms. If there are n output classes, n classifiers will be trained in parallel considering there is always a separation between an actual class and the remaining ones.
This approach is relatively lightweight at most, n-1 checks are needed to find the right class, so it has an O n complexity and, for this reason, it's normally the default choice and there's no need for further actions. One-vs-one The alternative to one-vs-all is training a model for each pair of classes. The complexity is no longer linear it's O n2 indeed and the right class is determined by a majority vote.
In general, this choice is more expensive and should be adopted only when a full dataset comparison is not preferable. Learnability A parametric model can be split into two parts: a static structure and a dynamic set of parameters. The former is determined by choice of a specific algorithm and is normally immutable except in the cases when the model provides some re-modeling functionalities , while the latter is the objective of our optimization.
Considering n unbounded parameters, they generate an n- dimensional space imposing bounds results in a sub-space without relevant changes in our discussion where each point, together with the immutable part of the estimator function, represents a learning hypothesis H associated with a specific set of parameters :. The goal of a parametric learning process is to find the best hypothesis whose corresponding prediction error is minimum and the residual generalization ability is enough to avoid overfitting.
In the following figure, there's an example of a dataset whose points must be classified as red Class A or blue Class B. Think about an n-dimensional binary classification problem. We say that the dataset X is linearly separable without transformations if there exists a hyperplane which divides the space into two subspaces containing only elements belonging to the same class. Removing the constraint of linearity, we have infinite alternatives using generic hypersurfaces.
However, a parametric model adopts only a family of non-periodic and approximate functions whose ability to oscillate and fit the dataset is determined sometimes in a very complex way by the number of parameters. The blue classifier is linear while the red one is cubic. At a glance, non-linear strategy seems to perform better, because it can capture more expressivity, thanks to its concavities. However, if new samples are added following the trend defined by the last four ones from the right , they'll be completely misclassified.
In fact, while a linear function is globally better but cannot capture the initial oscillation between 0 and 4, a cubic approach can fit this data almost perfectly but, at the same time, loses its ability to keep a global linear trend.
Therefore, there are two possibilities:. In this case, a linear or lower-level model will drive to underfitting, because it won't be able to capture an appropriate level of expressivity. If we think that future data can be locally distributed differently but keeps a global trend, it's preferable to have a higher residual misclassification error as well as a more precise generalization ability.
Using a bigger model focusing only on training data can drive to overfitting. Underfitting and overfitting The purpose of a machine learning model is to approximate an unknown function that associates input elements to output ones for a classifier, we call them classes. However, a training set is normally a representation of a global distribution, but it cannot contain all possible elements; otherwise the problem could be solved with a one-to-one association.
In the same way, we don't know the analytic expression of a possible underlying function, therefore, when training, it's necessary to think about fitting the model but keeping it free to generalize when an unknown input is presented. Unfortunately, this ideal condition is not always easy to find and it's important to consider two different dangers:. Underfitting: It means that the model isn't able to capture the dynamics shown by the same training set probably because its capacity is too limited.
Overfitting: the model has an excessive capacity and it's not more able to generalize considering the original dynamics provided by the training set. It can associate almost perfectly all the known samples to the corresponding output values, but when an unknown input is presented, the corresponding prediction error can be very high. Underfitting is easier to detect considering the prediction error, while overfitting may prove to be more difficult to discover as it could be initially considered the result of a perfect fitting.
Cross-validation and other techniques that we're going to discuss in the next chapters can easily show how our model works with test samples never seen during the training phase. That way, it would be possible to assess the generalization ability in a broader context remember that we're not working with all possible values, but always with a subset that should reflect the original distribution.
However, a generic rule of thumb says that a residual error is always necessary to guarantee a good generalization ability, while a model that shows a validation accuracy of Error measures In general, when working with a supervised scenario, we define a non- negative error measure em which takes two arguments expected and predicted output and allows us to compute a total error value over the whole dataset made up of n samples :.
This value is also implicitly dependent on the specific hypothesis H through the parameter set, therefore optimizing the error implies finding an optimal hypothesis considering the hardness of many optimization problems, this is not the absolute best one, but an acceptable approximation. In many cases, it's useful to consider the mean square error MSE :. Its initial value represents a starting point over the surface of a n-variables function.
A generic training algorithm has to find the global minimum or a point quite close to it there's always a tolerance to avoid an excessive number of iterations and a consequent risk of overfitting. This measure is also called loss function because its value must be minimized through an optimization problem.
When it's easy to determine an element which must be maximized, the corresponding loss function will be its reciprocal. A helpful interpretation of a generic and continuous loss function can be expressed in terms of potential energy:. In the following figure, there's a schematic representation of some different situations:. Just like in the physical situation, the starting point is stable without any external perturbation, so to start the process, it's needed to provide initial kinetic energy.
However, if such an energy is strong enough, then after descending over the slope the ball cannot stop in the global minimum. The residual kinetic energy can be enough to overcome the ridge and reach the right valley.
There are many techniques that have been engineered to solve this problem and avoid local minima. Following is what you need for this book: Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. With the following software and hardware list you can run all code files present in the book Chapter Click here to download it.
Python Deep Learning [Packt] [Amazon]. Click here if you have any feedback or suggestions. Skip to content. Star Branches Tags. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation.
Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour.
Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner.
Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.
If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model.
A basic knowledge of machine learning is preferred to get the best out of this guide. Master concepts related to algorithm tuning, parameter optimization, and. Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.
You must understand the algorithms to get good and be recognized as being good at machine learning.
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